File size: 1,350 Bytes
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license: apache-2.0
tags: [chess, reinforcement-learning, grpo]
---
# model_680m_6.4B — RL (GRPO) checkpoints
RL post-training trajectory for the chess pre-to-post compute-allocation study.
The pretraining base and SFT init for this model are
[`model_680m_6.4B`](https://huggingface.co/pavelslab-nyu/Chess-Pretrain-Models/tree/main/model_680m_6.4B) and
[`model_680m_6.4B`](https://huggingface.co/pavelslab-nyu/Chess-SFT-Models/tree/main/model_680m_6.4B).
| | |
|---|---|
| parameters | 680m |
| pretraining tokens | 6,397,966,000 (6.4B) |
| checkpoints here | 10 (steps 100–1000) |
| checkpoints saved by the run | 20 |
## Steps
`100`, `200`, `300`, `400`, `500`, `600`, `700`, `800`, `900`, `1000`
## Loading
Each `global_step_N/` folder is self-contained. The models use a custom
tokenizer (`tokenizer.py`), and the remote-code resolver ignores `subfolder=`,
so download the folder first and load the **local path**:
```python
from huggingface_hub import snapshot_download
from transformers import AutoModelForCausalLM, AutoTokenizer
step = "global_step_1000"
p = snapshot_download("Pre2Post-Chess-RL/Chess-RL-Models", allow_patterns=f"model_680m_6.4B/{step}/*") + f"/model_680m_6.4B/{step}"
model = AutoModelForCausalLM.from_pretrained(p, trust_remote_code=True)
tok = AutoTokenizer.from_pretrained(p, trust_remote_code=True)
```
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